The present invention relates to methods and systems for finding and ranking entities (e.g., a concept, location, person, company, industry, occupation, document, product, item, and the like, including occupational jargon expressed as single terms or phrases) contained within a text-based inquiry received from a user. The inquiries may be, for example email inquiries submitted to a help-desk, technical support inquiries submitted via an online chat session, or a request or search query submitted via a recommendation system, and the like, or may comprise any type of human generated free text.
Recommendation systems typically maintain (i) a database of declared/explicit profiles of professionals, and (ii) a history of actions taken by professionals within a given field of expertise, and use the combination of (i) and (ii) to recommend, for example, items or peers or additional services. The item recommendations can include, for example, documents to be reviewed, news items and industry announcements, events to attend, search keywords that may be of interest, and the like. A key purpose of the recommendations is to increase engagement between the industry professionals and the entity providing the recommendations by maximizing the value of the recommendations to the end-user (the industry professional). One example of a recommendation engine is disclosed in Applicant's U.S. Pat. No. 8,661,034 entitled “Bimodal Recommendation Engine for Recommending Items and Peers”, which is incorporated herein and made a part hereof by reference and which discloses methods, apparatus and algorithms for recommending items and/or peers in response to keyword searches.
Prior art methods and systems exist relating to entity extraction from text. For example, Stanford University has developed a method for Non Domain Specific Entity Recognition (see http://nlp.stanford.edu:8080/ner/). This prior art system uses closed lists and cannot find new names or other new terms. This system is also not domain specific.
It would be advantageous to provide an entity extraction system which is domain specific and which not only finds the entities, but also ranks them accurately according to importance within the text and within the corresponding domain. It would also be advantageous to provide an entity extraction system which does not rely on closed lists but which instead continuously evolves and expands its entity database over time based on inquiries it receives.
The methods and systems of the present invention provide the foregoing and other advantages.